Türkiye'nin Rize İli Çay Bahçelerinde Yaygın Bir Yabancı Ot Olarak Commelina communis L.’nin YOLO Tabanlı Tespiti

Abstract

Commelina communis L. (Asya gün çiçeği), birçok tarımsal üretim sisteminde verimliliği olumsuz etkileyen istilacı bir yabancı ot türüdür. Bu yabancı ot, Rize ili çay üretim alanlarında oldukça yaygın olarak bulunmaktadır. Bu çalışmanın amacı, C. communis’in tarla ve laboratuvar görüntülerinde otomatik olarak tanınmasını sağlamak üzere YOLO tabanlı nesne tespiti modellerinin geliştirilmesi ve performanslarının karşılaştırılmasıdır. Bu kapsamda iki farklı deneysel yaklaşım uygulanmıştır. İlk olarak, Google Colab platformu kullanılarak YOLOv5’in dört farklı varyantı (n, s, m, l) eğitilmiş ve Precision, Recall ve mAP@0.5 gibi performans ölçütleri ile değerlendirilmiştir. Bu modeller arasında YOLOv5l, %96 doğruluk, %96 duyarlılık, %96 F1 skoru ve %97 mAP@0.5 değeri ile en yüksek genel performansı göstermiştir. Onu yakından YOLOv5m takip etmiştir. En küçük ve en hızlı model olan YOLOv5n ise en düşük tespit doğruluğunu sergilemiştir. İkinci olarak, Roboflow platformu üzerinden 110 etiketlenmiş tarla görüntüsü kullanılarak üç alternatif model (YOLO-NAS, YOLOv11 ve YOLOv12) eğitilmiştir. Bu modeller arasında YOLO-NAS Accurate modeli, %96 mAP@0.5, %91 kesinlik ve %97 duyarlılık değerleriyle en iyi sonuçları vermiştir. YOLOv11 modeli %97 mAP@0.5 değeri ile onu yakından takip etmiş, en hızlı çıkarım süresine sahip olan YOLOv12 ise %92 mAP@0.5 ile biraz daha düşük bir doğruluk göstermiştir. Genel olarak, bu çalışma tüm modellerin C. communis tespiti açısından yüksek performans sergilediğini ortaya koymuştur. YOLOv5l, YOLOv11, YOLOv12 ve YOLO-NAS Accurate modelleri en doğru sonuçları sağlayan modeller olmuştur. Elde edilen bulgular, YOLO tabanlı derin öğrenme modellerinin tarımsal alanlarda istilacı yabancı otların hızlı, masrafsız ve doğru bir şekilde tespiti için etkili bir şekilde kullanılabileceğini ve hassas yabancı ot yönetim sistemlerine entegre edilebileceğini doğrulamaktadır.

Keywords

• Görüntü işleme
• Yapay zekâ
• Tarla ve laboratuvar görüntüleri
• Nesne tespiti
• Derin öğrenme

Project Number

YOK

Ethical Statement

There is no need to obtain permission from the ethics committee for this study.

YOLO-Powered Detection of Commelina communis L. as an Invasive Weed in Tea Gardens of Rize, Türkiye

Abstract

Commelina communis L. (Asiatic dayflower) is an invasive weed species that negatively affects crop productivity in many agricultural systems. This weed species is highly prevalent in tea-growing regions of Rize Province. This study aimed to develop and compare the performance of YOLO-based object detection models to automate the identification of C. communis in field imagery and laboratory. Two experimental frameworks were implemented. First, four YOLOv5 variants (n, s, m, l) were trained using the Google Colab platform and evaluated based on performance metrics, including Precision, Recall, and mAP@0.5. Among the models, YOLOv5l demonstrated the best overall performance with a Precision of 0.96, Recall of 0.96, F1 score of 0.96, and mAP@0.5 of 0.97, followed closely by YOLOv5m. YOLOv5n, the smallest and fastest model, showed the lowest detection accuracy. Secondly, three alternative models (YOLO-NAS, YOLOv11 and YOLOv12) were trained on 110 annotated field images via the Roboflow platform. The YOLO-NAS Accurate model outperformed the others with a mAP@0.5 of 96%, a Precision of 91%, and a Recall of 97.0%. YOLOv11 followed closely (mAP@0.5: 97%), while YOLOv12, though the fastest in inference, yielded slightly lower accuracy (mAP@0.5: 92%). Overall, the study demonstrated that all models achieved high performance in the detection of C. communis, with YOLOv5l, YOLOv11, YOLOv12, and YOLO-NAS Accurate producing the most accurate results. These findings confirm that YOLO-based deep learning models can be effectively utilized for very fast, at no cost, and accurate weed detection in agricultural settings, supporting their integration into precision weed management systems.

Keywords

• Image processing
• Artificial intelligence
• Field and laboratory imagery
• Object detection
• Deep learning

Project Number

YOK

Ethical Statement

There is no need to obtain permission from the ethics committee for this study.

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